applied sciences Article A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics Mykola Beshley 1 , Natalia Kryvinska 2,* , Oleg Yaremko 1 and Halyna Beshley 1 1 Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, 79013 Lviv, Ukraine; [email protected] (M.B.); [email protected] (O.Y.); [email protected] (H.B.) 2 Department of Information Systems, Faculty of Management, Comenius University, 25 82005 Bratislava, Slovakia * Correspondence: [email protected] Abstract: With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and Citation: Beshley, M.; Kryvinska, N.; optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a Yaremko, O.; Beshley, H. A Self- wireless access node in a heterogeneous environment. A structural diagram of the optimization stages Optimizing Technique Based on for wireless heterogeneous networks was developed, making it possible to improve the efficiency of Vertical Handover for Load Balancing their functioning. A model for studying the processes of functioning of a heterogeneous network in Heterogeneous Wireless Networks environment is proposed. This model uses the methodology of big data evaluation to perform data Using Big Data Analytics. Appl. Sci. transmission monitoring, analysis of tasks generated by network users, and statistical output of 2021, 11, 4737. https://doi.org/ 10.3390/app11114737 vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the Academic Editor: Murad Khan algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of Received: 14 April 2021 the heterogeneous network is increased by 16% when using the method of static reservation of Accepted: 20 May 2021 network resources, compared to homogeneous networks, and another 13% when using a uniform Published: 21 May 2021 distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load Publisher’s Note: MDPI stays neutral balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users. with regard to jurisdictional claims in published maps and institutional affil- Keywords: big data (BD); heterogeneous wireless networks (HWN); quality of service (QoS); self- iations. optimization; load balancing (LB); vertical handover (VHO); mobile network operators (MNOs) Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. 1.1. Background and Problem Statement This article is an open access article distributed under the terms and Today, the volume of mobile traffic is growing rapidly due to the total spread of a conditions of the Creative Commons variety of mobile devices [1]. The main volume of network traffic is mobile video on the Attribution (CC BY) license (https:// internet, social media and popular services of the Internet of Things. Therefore, a solution creativecommons.org/licenses/by/ is needed that will enable the operator to move to a centralized and flexible heterogeneous 4.0/). Appl. Sci. 2021, 11, 4737. https://doi.org/10.3390/app11114737 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 4737 2 of 24 network architecture in which resource management plays a crucial role, using the latest advances in information storage and cloud computing [2–4]. A large number of mobile devices are widely used and produce huge amounts of data every day [5]. This has a profound impact on society and social interaction and creates enormous challenges for Mobile Network Operators (MNOs) [6]. The volume, rate and variety of data from both mobile users and communication networks are increasing exponentially [7–10]. Accordingly, in the near future, there will be a need for data collection and analysis to make decisions about flexible resource management in heterogeneous systems. This will allow MNOs to analyze and predict the behavior and requirements of mobile users, which, in turn, will enable intelligent, real-time decision making across a wide range of applications [11,12]. By analyzing these data, mobile networks can actually provide and support a variety of intelligent services. New technologies are needed to process big data in a widescale, cost-effective and unstructured way [13]. Information about the unique characteristics of big data in mobile heterogeneous networks is an important element because it is critical to optimizing it [14]. Developing analytical methods will help MNOs to track and analyze different types of data, as well as resource status messages, across networks. Management logic and important statistics can be obtained from instantaneous data as well as from data in the history of statistics collected [15]. Useful information, such as the relationship between user behavior and network traffic, can help MNOs not only make decisions based on long-term strategies, but also optimize resource allocation to minimize allocations and operating costs [16]. An important challenge, however, is understanding the requirements of using big data (BD) analytics to deliver user services with quality of service and ensure highly efficient use of resources in future heterogeneous mobile networks. 1.2. Motivation The increasing range of services, infrastructure and traffic volumes puts forward the scientific challenge of improving the performance of wireless heterogeneous network systems (2G/3G/4G/5G/Wi-Fi) and user quality of service by improving the method of adaptive radio access system selection and developing a collaborative resource manage- ment model, using big data technology. 1.3. Our Contributions The main novelty of our work is that we proposed a model for the study of the processes of functioning of a heterogeneous network environment, which, in contrast to the known, uses the methodology of processing big data to perform the monitoring of data transmissions, analysis of tasks that are formed by network users and statistical data output on the initiation of vertical handover in the (2G/3G/4G/5G/Wi-Fi) infrastructure of mobile communications, allowing to study the optimization of the operators’ network by implementing an algorithm for redistribution of its network resources and providing flexible load balancing. Our contributions can be summarized as follows: • The problem of ensuring the effective functioning of a heterogeneous radio access network is formalized; • The method of increasing the efficiency of functioning of heterogeneous mobile com- munication networks based on big data technology is developed; • The realization of technologies for processing big data volumes, obtained by simulat- ing the process of functioning of a heterogeneous network is carried out; • The assessment of the effectiveness of the proposed solutions in relation to the opti- mization problem of the resources of a heterogeneous network of mobile communica- tion is carried out. The remainder of this paper is organized as follows: Section2 presents the related works; Section3 presents the proposed solution in the paper, including the description of the self-optimizing technique based on vertical handover for load balancing in hetero- Appl. Sci. 2021, 11, 4737 3 of 24 geneous wireless networks, using big data analytics; Section4 presents the experimental results; and Section5 presents the conclusions of the study. 2. Related Work The classical vertical handover mechanism in heterogeneous network selection aims to choose the optimal network solution for the user; however, this may lead to partial networks accessing too many users, overloading the network and influencing the QoS of the customer. The network load-balancing approach presented in the paper [17] transforms the network-balancing problem into an optimization problem by constructing a network allocation matrix in the network that meets the user’s needs. The optimal allocation method is then obtained, using the optimization algorithm to effectively reach the balanced network utilization. In addition, this method is used to weight different networks to provide QoS requirements of different services. The modeling results demonstrate the effectiveness of the suggested algorithm. The proposed approach is a generic algorithm that can be applied to various heterogeneous networks, such as public wireless networks. Su et al. [18] adopted a comparison method that compares two parameters,